We report the application of machine learning to smartphone-based colorimetric detection of pH values. The strip images were used as the training set for Least Squares-Support Vector Machine (LS-SVM) classifier algorithms that were able to successfully classify the distinct pH values. The difference in the obtained image formats was found not to significantly affect the performance of the proposed machine learning approach. Moreover, the influence of the illumination conditions on the perceived color of pH strips was investigated and further experiments were conducted to study the effect of color change on the learning model. Non-integer pH levels are identified as their nearest integer pH values, whereas the test results for integer pH levels using JPEG, RAW and RAW-corrected image formats captured under different lighting conditions lead to perfect classification accuracy, sensitivity and specificity, which proves that colorimetric detection using machine learning based systems is able to adapt to various experimental conditions and is a great candidate for smartphone-based sensing in paper-based colorimetric assays.
In
this paper, we present a smartphone platform for colorimetric
water quality detection based on the use of built-in camera for capturing
a single-use reference image. A custom-developed app processes this
image for training and creates a reference model to be used later
in real experimental conditions to calculate the concentration of
the unknown solution. This platform has been tested on four different
water quality colorimetric assays with various concentration levels,
and results show that the presented platform provides approximately
100% accuracy for colorimetric assays with noticeable color difference.
This portable, cost-effective, and user-friendly platform is promising
for application in water quality monitoring.
In this study, a facile strategy for fabricating a microfluidic flow cytometer using two glass micropipettes with different sizes and a 3D printed millifluidic aligner was presented. Particle confinement was achieved by hydrodynamic focusing using a single sample and sheath flow. Device performance was extracted using the forward and side-scattered optical signals obtained using fiber-coupled laser and photodetectors. The 3-D printing assisted glass capillary microfluidic device is ultra-low-cost, not labor-intensive and takes less than 10 min to fabricate. The present device offers a great alternative to conventional benchtop flow cytometers in terms of optofluidic configuration.
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